Senior ML Engineer

Boardroom Appointments; Cape Town; On-site; ZAR 900,000 - 1,500,000; Full time

Job summary

Boardroom Appointments seeks an experienced ML Engineer to design, deploy, and optimize real-time ML models in AWS SageMaker and EKS. You will build CI/CD pipelines and ensure observability, reliability, and compliance in production environments.

You will collaborate with data scientists and engineers to align ML initiatives with business goals, automate retraining and monitoring, and take ownership of ML solutions across the stack.

Qualifications

• 5+ years in Machine Learning Engineering or ML platform roles.
• Strong Python, PySpark, SQL and ML libraries (TF/PyTorch/Scikit-learn).
• Hands-on experience with AWS ML services and MLOps tools.

Responsibilities

• Design, develop, and deploy ML models for real-time decisioning.
• Build and maintain CI/CD pipelines for ML deployments.
• Automate retraining, monitoring, and logging in production.
• Ensure regulatory and security compliance.
• Collaborate with data scientists and engineers to align ML with business needs.
• Own ML solutions and guide junior engineers.

Skills

Python

PySpark

SQL

TensorFlow

PyTorch

Scikit-learn

AWS SageMaker

AWS EKS

AWS Lambda

AWS Redshift

Terraform

Control-M

Docker

Kubernetes

GitHub Actions

OpenSearch

Prometheus

Grafana

CloudWatch

Tools

AWS SageMaker

AWS EKS

Lambda

Redshift

Terraform

Control-M

Docker

Kubernetes

GitHub Actions

OpenSearch

FluentBit

Kibana

Prometheus

Grafana

CloudWatch

Job description

Design, develop, and deploy ML models in AWS SageMaker and EKS.

Optimize ML models for real-time decisioning in high-traffic environments.

Ensure models comply with regulatory and security standards.

Build and maintain CI/CD pipelines for ML model deployments.

Automate model retraining, monitoring, and logging using AWS Lambda, Terraform, and Control-M jobs.

Implement observability tools like OpenSearch, FluentBit, Prometheus, Kibana, Grafana, and AWS CloudWatch.

Develop ETL/ELT pipelines for data preprocessing and feature engineering.

Work with AWS Redshift to process large-scale datasets for model training.

Monitor ML models running 24/7 in production, ensuring reliability and high availability.

Work closely with engineering teams to troubleshoot and optimize production systems.

Participate in an on-call rotation for urgent ML pipeline issues.

Collaborate with data scientists, decision engineers, and credit engineers to align ML solutions with business needs.

Take ownership of ML solutions and provide guidance to junior engineers.

Contribute to the ongoing AI/ML strategy within the business.

Technical Skills:

• 5+ years of experience in Machine Learning Engineering.
• Strong expertise in Python, PySpark, SQL, and ML libraries (TensorFlow, PyTorch, Scikit-learn).
• Experience with AWS ML services (Amazon SageMaker, EKS, Lambda, Redshift, Control-M, Terraform).
• Experience with MLOps practices (CI/CD pipelines with GitHub Actions, Docker, Kubernetes).
• Proficiency in observability & monitoring tools: OpenSearch, FluentBit, Kibana, Prometheus, Grafana, CloudWatch.
• Strong understanding of real-time ML applications in financial environments.
• Experience in building and maintaining ETL pipelines in a cloud environment.

Soft Skills:

Leadership & Ownership Ability to work independently and drive ML initiatives.

Problem-Solving Ability to troubleshoot ML model failures in production.

Strong Communication Work effectively with cross-functional teams.

Agility Adapt to a fast-paced, high-stakes environment.